---
title: Slack summariser
subtitle: Build an agent that summarizes Slack channel messages
image: "https://og.composio.dev/api/og?title=Slack%20summariser"   # image for socials
description: Build a slack summarizer using Composio and Langchain
keywords: slack, summariser, utility agent
---

With Composio's managed authentication and tool calling, it's easy to build the
AI agents that can interact with the real world while reducing the boilerplate
required to setup and manage the authentication. This cookbook will walk you through
the process of building agents using `Composio`,  `LangChain`.

## Prerequisites

* Python3.x
* [UV](https://docs.astral.sh/uv/getting-started/installation/)
* Composio API key
* OpenAI API key
* Understanding of building AI agents (Preferably with LangChain)

## Build slack agent

Let's start by building an agent that can interact with your slack workspace using Composio.

```python
from composio import Composio
from composio_langchain import LangchainProvider

from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI


def create_agent(user_id: str, composio_client: Composio[LangchainProvider]):
    """
    Create an agent for a given user id.
    """
    # Step 1: Get all the tools
    tools = composio_client.tools.get(
        user_id=user_id,
        toolkits=["SLACK"],
    )

    # Step 2: Pull relevant agent prompt.
    prompt = hub.pull("hwchase17/openai-functions-agent")

    # Step 3: Initialize chat model.
    openai_client = ChatOpenAI(model="gpt-5")

    # Step 4: Define agent
    return AgentExecutor(
        agent=create_openai_functions_agent(
            openai_client,
            tools,
            prompt,
        ),
        tools=tools,
        verbose=True,
    )
```


## Authenticating users

To authenticate your users with Composio you need an auth config for the given
app, In this case you need one for slack. You can create and manage auth configs
from the [dashboard](https://platform.composio.dev/?next_page=/auth-configs?create_auth_config=slack).

Composio platform provides composio managed authentication for some apps to help
you fast-track your development, `slack` being one of them. You can use these
default auth configs for development, but for production you should always use
your own oauth app configuration.

Using dashboard is the preferred way of managing authentication configs, but if
you want to do it manually you can follow the guide below

<details>

<summary>Click to expand</summary>

---

To create an authentication config for `slack` you need `client_id` and `client_secret`
from your Slack [App](https://api.slack.com/apps). Once you have the required credentials
you can use the following piece of code to set up authentication for `slack`. 

```python
from composio import Composio
from composio_langchain import LangchainProvider

def create_auth_config(composio_client: Composio[OpenAIProvider]):
    """
    Create a auth config for the slack toolkit.
    """
    client_id = os.getenv("SLACK_CLIENT_ID")
    client_secret = os.getenv("SLACK_CLIENT_SECRET")
    if not client_id or not client_secret:
        raise ValueError("SLACK_CLIENT_ID and SLACK_CLIENT_SECRET must be set")

    return composio_client.auth_configs.create(
        toolkit="SLACK",
        options={
            "name": "default_slack_auth_config",
            "type": "use_custom_auth",
            "auth_scheme": "OAUTH2",
            "credentials": {
                "client_id": client_id,
                "client_secret": client_secret,
            },
        },
    )
```

This will create an authentication config for `slack` which you can use to
authenticate your users for your app. Ideally you should just create one
authentication object per project, so check for an existing auth config
before you create a new one.

```python
def fetch_auth_config(composio_client: Composio[OpenAIProvider]):
    """
    Fetch the auth config for a given user id.
    """
    auth_configs = composio_client.auth_configs.list()
    for auth_config in auth_configs.items:
        if auth_config.toolkit == "SLACK":
            return auth_config

    return None
```
</details>

Once you have authentication management in place, we can start with connecting
your users to your `slack` app. Let's implement a function to connect the users
to your `slack` app via composio.

```python

# Function to initiate a connected account
def create_connection(composio_client: Composio[OpenAIProvider], user_id: str):
    """
    Create a connection for a given user id and auth config id.
    """
    # Fetch or create the auth config for the slack toolkit
    auth_config = fetch_auth_config(composio_client=composio_client)
    if not auth_config:
        auth_config = create_auth_config(composio_client=composio_client)

    # Create a connection for the user
    return composio_client.connected_accounts.initiate(
        user_id=user_id,
        auth_config_id=auth_config.id,
    )
```

Now, when creating tools for your agent always check if the user already has a
connected account before creating a new one.

```python
def check_connected_account_exists(
    composio_client: Composio[LangchainProvider],
    user_id: str,
):
    """
    Check if a connected account exists for a given user id.
    """
    # Fetch all connected accounts for the user
    connected_accounts = composio_client.connected_accounts.list(
        user_ids=[user_id],
        toolkit_slugs=["SLACK"],
    )

    # Check if there's an active connected account
    for account in connected_accounts.items:
        if account.status == "ACTIVE":
            return True

        # Ideally you should not have inactive accounts, but if you do, you should delete them
        print(f"[warning] inactive account {account.id} found for user id: {user_id}")
    return False
```

## Modifiers

In the current setup, we are expanding too much unnecessary tokens because response
from `SLACK_FETCH_CONVERSATION_HISTORY` tool call contains too much unnecessary
information. This can be fixed using `after_execute` modifier. An after execute
modifier is called after a tool execution is complete, here you can process
and modify the response object to make it more easy to consume for your agent.

```python
from composio import after_execute
from composio.types import ToolExecutionResponse


@after_execute(tools=["SLACK_FETCH_CONVERSATION_HISTORY"])
def clean_conversation_history(
    tool: str,
    toolkit: str,
    response: ToolExecutionResponse,
) -> ToolExecutionResponse:
    """
    Clean the conversation history.
    """
    if not response["data"]["ok"]:
        return response

    try:
        response["data"]["messages"] = [
            {"user": message["user"], "text": message["text"]}
            for message in response["data"]["messages"]
            if message["type"] == "message"
        ]
    except KeyError:
        pass

    return response
```

To register modifiers, include them in the `composio.tools.get` call.

```python
tools = composio_client.tools.get(
    user_id=user_id,
    toolkits=[SLACK_TOOLKIT],
    modifiers=[clean_conversation_history],
)
```

## Putting everything together

So far, we have created an agent with ability to interact with your `slack` 
workspace using the `composio` SDK, functions to manage connected accounts
for users and a simple agent runner. Let's package this as a CLI tool.

```python
def run_agent(user_id: str, prompt: str):
    composio_client = Composio(provider=LangchainProvider())
    if not check_connected_account_exists(composio_client, user_id):
        connection_request = create_connection(composio_client, user_id)
        print(
            f"Authenticate with the following link: {connection_request.redirect_url}"
        )
        connection_request.wait_for_connection()

    agent = create_agent(user_id, composio_client)
    agent.invoke({"input": prompt})
```

To test the above function as CLI, follow the steps below

1. Clone the repository
   ```bash
   git clone git@github.com:composiohq/slack-summarizer
   cd slack-summarizer/
   ```
2. Setup environment
   ```bash
   cp .env.example .env
   ```

   Fill the api keys
   ```dotenv
   COMPOSIO_API_KEY=
   OPENAI_API_KEY=
   ```

   Create the virtual env
   ```bash
   make env
   source .venv/bin/activate
   ```
3. Run the agent
   ```bash
   python slack_summariser --user-id "default" --prompt "summarise last 5 messages from #general channel"
   ```

## Using Composio for managed auth and tools

Composio reduces a lot of boilerplate for building AI agents with ability access and use a wide variety of apps. For example in this cookbook, to build `slack` integration without composio you would have to write code to
- manage `slack` oauth app
- manage user connections
- tools for your agents to interact with `slack`

Using composio simplifies all of the above to a few lines of code as we've seen the cookbook.

## Best practices

**🔒 User Management**:

- Use unique, consistent `user_id` values for each person
- Each user maintains their own slack connection
- User IDs can be email addresses, usernames, or any unique identifier

## Troubleshooting

**Connection Issues**:
- Ensure your `.env` file has valid `COMPOSIO_API_KEY` and `OPENAI_API_KEY`
- Check that the user has completed `slack` authorization
- Verify the user_id matches exactly between requests

**API Errors**:
- Check the server logs for detailed error messages
- Ensure request payloads match the expected format
- Visit `/docs` endpoint for API schema validation
